Remote Sensing

Editorial

13 pages, 3204 KiB

Open AccessEditorial

Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”

by Chein-I Chang, Meiping Song, Chunyan Yu, Yulei Wang, Haoyang Yu, Jiaojiao Li, Lin Wang, Hsiao-Chi Li and Xiaorun Li

Remote Sens. 2022, 14(20), 5111; https://doi.org/10.3390/rs14205111 - 13 Oct 2022

Cited by 2 | Viewed by 1846

Abstract

Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel [...] Read more.

Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue “Advances in Hyperspectral Data Exploitation“ is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image Classification, II: Hyperspectral Target Detection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave Infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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17 pages, 9619 KiB

Open AccessArticle

A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

by Yulei Wang, Qingyu Zhu, Yao Shi, Meiping Song and Chunyan Yu

Remote Sens. 2021, 13(24), 4967; https://doi.org/10.3390/rs13244967 - 7 Dec 2021

Cited by 2 | Viewed by 2862

Abstract

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators [...] Read more.

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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20 pages, 1697 KiB

Open AccessArticle

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

by Zhao Wang, Fenlong Jiang, Tongfei Liu, Fei Xie and Peng Li

Remote Sens. 2021, 13(23), 4927; https://doi.org/10.3390/rs13234927 - 4 Dec 2021

Cited by 12 | Viewed by 3470

Abstract

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global [...] Read more.

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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20 pages, 27974 KiB

Open AccessArticle

Detection and Classification of Rice Infestation with Rice Leaf Folder (Cnaphalocrocis medinalis) Using Hyperspectral Imaging Techniques

by Gui-Chou Liang, Yen-Chieh Ouyang and Shu-Mei Dai

Remote Sens. 2021, 13(22), 4587; https://doi.org/10.3390/rs13224587 - 15 Nov 2021

Cited by 7 | Viewed by 3193

Abstract

The detection of rice leaf folder (RLF) infestation usually depends on manual monitoring, and early infestations cannot be detected visually. To improve detection accuracy and reduce human error, we use push-broom hyperspectral sensors to scan rice images and use machine learning and deep [...] Read more.

The detection of rice leaf folder (RLF) infestation usually depends on manual monitoring, and early infestations cannot be detected visually. To improve detection accuracy and reduce human error, we use push-broom hyperspectral sensors to scan rice images and use machine learning and deep neural learning methods to detect RLF-infested rice leaves. Different from traditional image processing methods, hyperspectral imaging data analysis is based on pixel-based classification and target recognition. Since the spectral information itself is a feature and can be considered a vector, deep learning neural networks do not need to use convolutional neural networks to extract features. To correctly detect the spectral image of rice leaves infested by RLF, we use the constrained energy minimization (CEM) method to suppress the background noise of the spectral image. A band selection method was utilized to reduce the computational energy consumption of using the full-band process, and six bands were selected as candidate bands. The following method is the band expansion process (BEP) method, which is utilized to expand the vector length to improve the problem of compressed spectral information for band selection. We use CEM and deep neural networks to detect defects in the spectral images of infected rice leaves and compare the performance of each in the full frequency band, frequency band selection, and frequency BEP. A total of 339 hyperspectral images were collected in this study; the results showed that six bands were sufficient for detecting early infestations of RLF, with a detection accuracy of 98% and a Dice similarity coefficient of 0.8, which provides advantages of commercialization of this field. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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23 pages, 27690 KiB

Open AccessArticle

Rice Leaf Blast Classification Method Based on Fused Features and One-Dimensional Deep Convolutional Neural Network

by Shuai Feng, Yingli Cao, Tongyu Xu, Fenghua Yu, Dongxue Zhao and Guosheng Zhang

Remote Sens. 2021, 13(16), 3207; https://doi.org/10.3390/rs13163207 - 13 Aug 2021

Cited by 20 | Viewed by 3540

Abstract

Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production [...] Read more.

Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production management. Hyperspectral imaging technology is a type of crop disease identification method with great potential. However, a large amount of redundant information mixed in hyperspectral data makes it more difficult to establish an efficient disease classification model. At the same time, the difficulty and small scale of agricultural hyperspectral imaging data acquisition has resulted in unrepresentative features being acquired. Therefore, the focus of this study was to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. First, the hyperspectral imaging data were pre-processed in order to extract rice leaf samples of five disease classes, and the number of samples was increased by data augmentation methods. Secondly, spectral feature wavelengths, vegetation indices and texture features were obtained based on the amplified sample data. Thirdly, seven one-dimensional deep convolutional neural networks (DCNN) models were constructed based on spectral feature wavelengths, vegetation indices, texture features and their fusion features. Finally, the model in this paper was compared and analyzed with the Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The results showed that the classification model constructed using fused features was significantly better than the model constructed with a single feature in terms of accuracy in grading the degree of leaf blast disease. The best performance was achieved with the combination of the successive projections algorithm (SPA) selected feature wavelengths and texture features (TFs). The modeling results also show that the DCNN model provides better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1 and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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17 pages, 5795 KiB

Open AccessArticle

Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data

by Andrew Hennessy, Kenneth Clarke and Megan Lewis

Remote Sens. 2021, 13(12), 2243; https://doi.org/10.3390/rs13122243 - 8 Jun 2021

Cited by 8 | Viewed by 5330

Abstract

New, accurate and generalizable methods are required to transform the ever-increasing amount of raw hyperspectral data into actionable knowledge for applications such as environmental monitoring and precision agriculture. Here, we apply advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation [...] Read more.

New, accurate and generalizable methods are required to transform the ever-increasing amount of raw hyperspectral data into actionable knowledge for applications such as environmental monitoring and precision agriculture. Here, we apply advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation data, whilst maintaining class relationships. Specifically, a Generative Adversarial Network (GAN) is trained using the Cramér distance on two vegetation hyperspectral datasets, demonstrating the ability to approximate the distribution of the training samples. Evaluation of the synthetic spectra shows that they respect many of the statistical properties of the real spectra, conforming well to the sampled distributions of all real classes. Creation of an augmented dataset consisting of synthetic and original samples was used to train multiple classifiers, with increases in classification accuracy seen under almost all circumstances. Both datasets showed improvements in classification accuracy ranging from a modest 0.16% for the Indian Pines set and a substantial increase of 7.0% for the New Zealand vegetation. Selection of synthetic samples from sparse or outlying regions of the feature space of real spectral classes demonstrated increased discriminatory power over those from more central portions of the distributions. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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22 pages, 3037 KiB

Open AccessArticle

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

by Wenjing Chen, Xiangtao Zheng and Xiaoqiang Lu

Remote Sens. 2021, 13(7), 1260; https://doi.org/10.3390/rs13071260 - 26 Mar 2021

Cited by 28 | Viewed by 4454

Abstract

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs [...] Read more.

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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23 pages, 3775 KiB

Open AccessArticle

AT²ES: Simultaneous Atmospheric Transmittance-Temperature-Emissivity Separation Using Online Upper Midwave Infrared Hyperspectral Images

by Sungho Kim, Jungsub Shin and Sunho Kim

Remote Sens. 2021, 13(7), 1249; https://doi.org/10.3390/rs13071249 - 25 Mar 2021

Cited by 8 | Viewed by 2711

Abstract

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (

A T^{2} E S

) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the [...] Read more.

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (

A T^{2} E S

) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TES using MODTRAN in the long wave infrared band. Simultaneous online atmospheric transmittance-temperature-emissivity separation starts with approximation of the radiative transfer equation in the upper midwave infrared band. The highest atmospheric band is used to estimate surface temperature, assuming high emissive materials. The lowest atmospheric band (CO

_{2}

absorption band) is used to estimate air temperature. Through onsite hyperspectral data regression, atmospheric transmittance is obtained from the y-intercept, and emissivity is separated using the observed radiance, the separated object temperature, the air temperature, and atmospheric transmittance. The advantage with the proposed method is from being the first attempt at simultaneous

A T^{2} E S

and online separation without any prior knowledge and pre-processing. Midwave Fourier transform infrared (FTIR)-based outdoor experimental results validate the feasibility of the proposed

A T^{2} E S

method. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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17 pages, 16410 KiB

Open AccessArticle

Residual Augmented Attentional U-Shaped Network for Spectral Reconstruction from RGB Images

by Jiaojiao Li, Chaoxiong Wu, Rui Song, Yunsong Li and Weiying Xie

Remote Sens. 2021, 13(1), 115; https://doi.org/10.3390/rs13010115 - 31 Dec 2020

Cited by 4 | Viewed by 3138

Abstract

Deep convolutional neural networks (CNNs) have been successfully applied to spectral reconstruction (SR) and acquired superior performance. Nevertheless, the existing CNN-based SR approaches integrate hierarchical features from different layers indiscriminately, lacking an investigation of the relationships of intermediate feature maps, which limits the [...] Read more.

Deep convolutional neural networks (CNNs) have been successfully applied to spectral reconstruction (SR) and acquired superior performance. Nevertheless, the existing CNN-based SR approaches integrate hierarchical features from different layers indiscriminately, lacking an investigation of the relationships of intermediate feature maps, which limits the learning power of CNNs. To tackle this problem, we propose a deep residual augmented attentional u-shape network (RA²UN) with several double improved residual blocks (DIRB) instead of paired plain convolutional units. Specifically, a trainable spatial augmented attention (SAA) module is developed to bridge the encoder and decoder to emphasize the features in the informative regions. Furthermore, we present a novel channel augmented attention (CAA) module embedded in the DIRB to rescale adaptively and enhance residual learning by using first-order and second-order statistics for stronger feature representations. Finally, a boundary-aware constraint is employed to focus on the salient edge information and recover more accurate high-frequency details. Experimental results on four benchmark datasets demonstrate that the proposed RA²UN network outperforms the state-of-the-art SR methods under quantitative measurements and perceptual comparison. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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20 pages, 4013 KiB

Open AccessArticle

Hyperspectral Nonlinear Unmixing by Using Plug-and-Play Prior for Abundance Maps

by Zhicheng Wang, Lina Zhuang, Lianru Gao, Andrea Marinoni, Bing Zhang and Michael K. Ng

Remote Sens. 2020, 12(24), 4117; https://doi.org/10.3390/rs12244117 - 16 Dec 2020

Cited by 12 | Viewed by 2878

Abstract

Spectral unmixing (SU) aims at decomposing the mixed pixel into basic components, called endmembers with corresponding abundance fractions. Linear mixing model (LMM) and nonlinear mixing models (NLMMs) are two main classes to solve the SU. This paper proposes a new nonlinear unmixing method [...] Read more.

Spectral unmixing (SU) aims at decomposing the mixed pixel into basic components, called endmembers with corresponding abundance fractions. Linear mixing model (LMM) and nonlinear mixing models (NLMMs) are two main classes to solve the SU. This paper proposes a new nonlinear unmixing method base on general bilinear model, which is one of the NLMMs. Since retrieving the endmembers’ abundances represents an ill-posed inverse problem, prior knowledge of abundances has been investigated by conceiving regularizations techniques (e.g., sparsity, total variation, group sparsity, and low rankness), so to enhance the ability to restrict the solution space and thus to achieve reliable estimates. All the regularizations mentioned above can be interpreted as denoising of abundance maps. In this paper, instead of investing effort in designing more powerful regularizations of abundances, we use plug-and-play prior technique, that is to use directly a state-of-the-art denoiser, which is conceived to exploit the spatial correlation of abundance maps and nonlinear interaction maps. The numerical results in simulated data and real hyperspectral dataset show that the proposed method can improve the estimation of abundances dramatically compared with state-of-the-art nonlinear unmixing methods. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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21 pages, 7717 KiB

Open AccessArticle

Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation

by Haoyang Yu, Xiao Zhang, Meiping Song, Jiaochan Hu, Qiandong Guo and Lianru Gao

Remote Sens. 2020, 12(20), 3342; https://doi.org/10.3390/rs12203342 - 13 Oct 2020

Cited by 4 | Viewed by 2205

Abstract

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample [...] Read more.

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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26 pages, 4237 KiB

Open AccessArticle

A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences

by Zhaoxu Li, Qiang Ling, Jing Wu, Zhengyan Wang and Zaiping Lin

Remote Sens. 2020, 12(17), 2783; https://doi.org/10.3390/rs12172783 - 27 Aug 2020

Cited by 7 | Viewed by 2849

Abstract

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, [...] Read more.

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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21 pages, 1803 KiB

Open AccessArticle

Toward Super-Resolution Image Construction Based on Joint Tensor Decomposition

by Xiaoxu Ren, Liangfu Lu and Jocelyn Chanussot

Remote Sens. 2020, 12(16), 2535; https://doi.org/10.3390/rs12162535 - 6 Aug 2020

Cited by 3 | Viewed by 3691

Abstract

In recent years, fusing hyperspectral images (HSIs) and multispectral images (MSIs) to acquire super-resolution images (SRIs) has been in the spotlight and gained tremendous attention. However, some current methods, such as those based on low rank matrix decomposition, also have a fair share [...] Read more.

In recent years, fusing hyperspectral images (HSIs) and multispectral images (MSIs) to acquire super-resolution images (SRIs) has been in the spotlight and gained tremendous attention. However, some current methods, such as those based on low rank matrix decomposition, also have a fair share of challenges. These algorithms carry out the matrixing process for the original image tensor, which will lose the structure information of the original image. In addition, there is no corresponding theory to prove whether the algorithm can guarantee the accurate restoration of the fused image due to the non-uniqueness of matrix decomposition. Moreover, degenerate operators are usually unknown or difficult to estimate in some practical applications. In this paper, an image fusion method based on joint tensor decomposition (JTF) is proposed, which is more effective and more applicable to the circumstance that degenerate operators are unknown or tough to gauge. Specifically, in the proposed JTF method, we consider SRI as a three-dimensional tensor and redefine the fusion problem with the decomposition issue of joint tensors. We then formulate the JTF algorithm, and the experimental results certify the superior performance of the proposed method in comparison to the current popular schemes. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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20 pages, 33575 KiB

Open AccessArticle

Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

by Radu-Mihai Coliban, Maria Marincaş, Cosmin Hatfaludi and Mihai Ivanovici

Remote Sens. 2020, 12(15), 2479; https://doi.org/10.3390/rs12152479 - 2 Aug 2020

Cited by 11 | Viewed by 5036

Abstract

The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques [...] Read more.

The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. In this article, we propose the usage of a linear model for color formation, to emulate the image acquisition process by a digital color camera. We show how the choice of spectral sensitivity curves has an impact on the visualization of hyperspectral images as RGB color images. In addition, we propose a non-linear model based on an artificial neural network. We objectively assess the impact and the intrinsic quality of the hyperspectral image visualization from the point of view of the amount of information and complexity: (i) in order to objectively quantify the amount of information present in the image, we use the color entropy as a metric; (ii) for the evaluation of the complexity of the scene we employ the color fractal dimension, as an indication of detail and texture characteristics of the image. For comparison, we use several state-of-the-art visualization techniques. We present experimental results on visualization using both the linear and non-linear color formation models, in comparison with four other methods and report on the superiority of the proposed non-linear model. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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34 pages, 31938 KiB

Open AccessArticle

Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

by Shih-Yu Chen, Chuan-Yu Chang, Cheng-Syue Ou and Chou-Tien Lien

Remote Sens. 2020, 12(15), 2348; https://doi.org/10.3390/rs12152348 - 22 Jul 2020

Cited by 23 | Viewed by 6100

Abstract

The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain [...] Read more.

The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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23 pages, 13694 KiB

Open AccessArticle

Novel Air Temperature Measurement Using Midwave Hyperspectral Fourier Transform Infrared Imaging in the Carbon Dioxide Absorption Band

by Sungho Kim

Remote Sens. 2020, 12(11), 1860; https://doi.org/10.3390/rs12111860 - 8 Jun 2020

Cited by 6 | Viewed by 3302

Abstract

Accurate visualization of air temperature distribution can be useful for various thermal analyses in fields such as human health and heat transfer of local area. This paper presents a novel approach to measuring air temperature from midwave hyperspectral Fourier transform infrared (FTIR) imaging [...] Read more.

Accurate visualization of air temperature distribution can be useful for various thermal analyses in fields such as human health and heat transfer of local area. This paper presents a novel approach to measuring air temperature from midwave hyperspectral Fourier transform infrared (FTIR) imaging in the carbon dioxide absorption band (between 4.25–4.35

μ

m). In this study, the proposed visual air temperature (VisualAT) measurement is based on the observation that the carbon dioxide band shows zero transmissivity at short distances. Based on analysis of the radiative transfer equation in this band, only the path radiance by air temperature survives. Brightness temperature of the received radiance can provide the raw air temperature and spectral average, followed by a spatial median-mean filter that can produce final air temperature images. Experiment results tested on a database obtained by a midwave extended FTIR system (Telops, Quebec City, QC, Canada) from February to July 2018 show a mean absolute error of 1.25

^{\circ}

K for temperature range of 2.6−26.4

^{\circ}

C. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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18 pages, 3545 KiB

Open AccessArticle

Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning

by Yao Liu, Lianru Gao, Chenchao Xiao, Ying Qu, Ke Zheng and Andrea Marinoni

Remote Sens. 2020, 12(11), 1780; https://doi.org/10.3390/rs12111780 - 1 Jun 2020

Cited by 40 | Viewed by 8259

Abstract

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group [...] Read more.

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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21 pages, 4780 KiB

Open AccessArticle

Underwater Hyperspectral Target Detection with Band Selection

by Xianping Fu, Xiaodi Shang, Xudong Sun, Haoyang Yu, Meiping Song and Chein-I Chang

Remote Sens. 2020, 12(7), 1056; https://doi.org/10.3390/rs12071056 - 25 Mar 2020

Cited by 29 | Viewed by 5461

Abstract

Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to [...] Read more.

Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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24 pages, 7976 KiB

Open AccessArticle

Deep Relation Network for Hyperspectral Image Few-Shot Classification

by Kuiliang Gao, Bing Liu, Xuchu Yu, Jinchun Qin, Pengqiang Zhang and Xiong Tan

Remote Sens. 2020, 12(6), 923; https://doi.org/10.3390/rs12060923 - 13 Mar 2020

Cited by 136 | Viewed by 6292

Abstract

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how [...] Read more.

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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13 pages, 8934 KiB

Open AccessTechnical Note

A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors

by Bikram Pratap Banerjee and Simit Raval

Remote Sens. 2021, 13(16), 3295; https://doi.org/10.3390/rs13163295 - 20 Aug 2021

Cited by 7 | Viewed by 2708

Abstract

Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles [...] Read more.

Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)

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